Objective To explore the immune stimulation effect of recombinant E.coli LLO/OVA on mice bone marrow-derived dendritic cells （BMDCs） and T lymphocytes in vitro.Methods After BMDCs stimulated by E.coli LLO/OVA,their ...Objective To explore the immune stimulation effect of recombinant E.coli LLO/OVA on mice bone marrow-derived dendritic cells （BMDCs） and T lymphocytes in vitro.Methods After BMDCs stimulated by E.coli LLO/OVA,their Toll-like receptor （TLR） and nucleotide-binding oligomerization domain （NOD） receptor signalling pathway were examined by superarray hybridization;and the priming effect of the vaccine activated BMDCs on CD4＋T and CD8＋T was determined by [3H]thymidine uptake and ELISA,the tumor cytotoxic effect of activated CD8＋T cells was determined by cytotoxic assay.Results After BMDCs were activated by E.coli LLO/OVA via TLR4,NOD1 receptor and NF-κB signalling pathway,the expression of their surface molecules including MHC class Ⅰ,MHC class Ⅱ,CD40,CD80 and CD86 significantly up-regulated;the secretion of IL-12 and IFN-？ increased also.The mature BMDCs stimulated the allergic CD4＋T and CD8＋T cells proliferation and their IL-2 and IFN-γ secretion,and the activated CD8＋T cells effectively killed B16-OVA melanoma cells and RMA-S/OVA lymphoma cells in vitro.Conclusion E.coli LLO/OVA is effective in inducing BMDCs maturation via activating TLR4 and NOD1 receptor signalling pathway and promoting specific anti-tumor T cell immunity in vitro.展开更多
This paper represents a comparative performance evaluation of different diversity combining techniques for a SIMO-OFDM （single-input-multiple-output orthogonal frequency division multiplexing） system over Rayleigh f...This paper represents a comparative performance evaluation of different diversity combining techniques for a SIMO-OFDM （single-input-multiple-output orthogonal frequency division multiplexing） system over Rayleigh fading channel. OFDM is a key technique for achieving high data rates and spectral efficiency requirements for wireless communication systems. But in scattering environment, the system performances are severely degraded by the effects of multipath fading and inter-symbol interference. In wireless communication systems, antenna diversity is an important technique to combat multipath fading in order to improve the system performance and increase the channel capacity. In this paper, the performance of different diversity combining techniques-SC （selection combining）, EGC （equal gain combining） and MRC （maximal ratio combining） has been analyzed and compared in terms of SNR （signal to noise ratio） and BER （bit error rate） probability. The simulation results show that the maximal ratio combining technique provides maximum performance improvement relative to all other combining schemes by maximizing the SNR of SIMO-OFDM system at the combiner output. The analytic expressions of error probability and effective bit energy to noise ratio correlated with BPSK （binary phase shift keying） modulation have been derived and formulated for N-branch SC, EGC and MRC schemes. The BER characteristics for all three combining techniques are simulated in MATLAB （matrix laboratory） tool box for varying bit energy to noise ratio. Our results also derives that SNR can be improved if the number of receiving antenna is increased, which in turn reduces BER over a Rayleigh fading channel.展开更多
The goal of this work is to evaluate and to give evidence to innovative and sustainable technologies applied in the construction industry to carry out self-sufficient energy and to use the surplus energy for the produ...The goal of this work is to evaluate and to give evidence to innovative and sustainable technologies applied in the construction industry to carry out self-sufficient energy and to use the surplus energy for the production of hydrogen vector. An architectural integration design along with high technological systems is performed. The intermittency of renewable energy sources along with climatic conditions dependency imposes to store the energy produced, since it is clean and having a big calorific value: the hydrogen vector is currently the better energy carrier. The energy to obtain hydrogen by dissociation of water is supplied by a photovoltaic (PV) system. Through the computations of the annual energy balance between building’s demand and supply energy, it is shown that the extra energy produced by the solar generation system is used also for the hydrogen sustainable mobility. The renewable systems, model’s design and case study are tackled for the bigger one of the Dodecanese islands in the South Aegean Sea: Rhodes (Rodos). The Zero energy building’s integrative design-based approach, applied to the Hotel Buildings type industry is targeted to have new hotels buildings, in the Mediterranean typical warm climate, with zero energy consumption. The designers, authors of this work, have studied a real case or pilot project of an hotel, in the resort formula, suitable to the Greek landscape, showcasing technologies and innovations supporting environmental sustainability, energy efficiency, use of renewable energy, electricity storage by fuel cells that are tools particularly applicable to hotel facility [1]. The feasibility of this case study or pilot project is aligned jointly to the target of Zero Emission and Energy Efficiency EU Policy, as imposed by EU Directives. The strategic position of Rhodes in a geographical point full of sun and wind renewable energy power, enables to ensure the clean energy production, the current interesting development of the hydrogen as energy vector in the buildings [2] and also to satisfy the demand of tourists’ accommodation by having at the same time zero energy costs. Moreover, the presence in the island of the best example worldwide of ancient and sustainable built environment (UNESCO World Heritage site), represents also the best motivation to give witness there of a zero impact environmental urban development through the adoption of these achieved scientific results for a major success of Zero Energy Buildings.展开更多
Polyurethanes(PUs)are among the most studied,manufactured,and employed polymers due to their versatility and wide range of applications.However,their synthesis generally relies on toxic,non-renewable,and harmful petro...Polyurethanes(PUs)are among the most studied,manufactured,and employed polymers due to their versatility and wide range of applications.However,their synthesis generally relies on toxic,non-renewable,and harmful petroleum-based chemicals.In recent decades,driven by urgent environmental needs,research activities for the development of alternative synthetic routes for their production have significantly increased,especially to find more sustainable raw materials and procedures that,for example,no longer require dangerous solvents.Given these premises,the main purpose of this review is to highlight the most recent advances in the production of bioderived polyurethanes.After briefly discussing the chemistry of polyurethanes,we focused on the generation of bio-polyols and bio-isocyanates from plant oils and lignocellulosic biomass(e.g.lignin and sugars),as well as on the most recent trends in non-isocyanates polyurethanes(NIPUs)production.Discussions on their fields of application will be key to giving readers an overview of the actual capabilities of these materials.This review aims to cover and discuss the most recent contributions appearing in the literature up to the beginning of 2023.展开更多
A general class of non-linear large-scale interconnected systems is considered,wherein each subsystem is comprised of a nominal part in a general strict-feedback-like structure and a set of appended dynamics.Parametri...A general class of non-linear large-scale interconnected systems is considered,wherein each subsystem is comprised of a nominal part in a general strict-feedback-like structure and a set of appended dynamics.Parametric and functional uncertainties and time delays are allowed throughout the overall system structure including the nominal strictfeedback-like parts and appended dynamics of each subsystem as well as the non-linear subsystem interconnections.The controller design is based on the dual dynamic highgain scaling technique and provides a robust adaptive delay-independent globally stabilising decentralised output-feedback controller.The disturbance attenuation properties of the proposed output-feedback decentralised controller to an exogenous disturbance input are also analysed and specific conditions under which properties such as Input-toOutput-practical-Stability and asymptotic stabilisation are attained are also discussed.展开更多
This review prepared for the fourth International Workshop on Tropical Cyclone Landfall Processes(IWTCLP-4) summarizes the most recent(2015-2017) theoretical and practical knowledge in the field of tropical cyclone(TC...This review prepared for the fourth International Workshop on Tropical Cyclone Landfall Processes(IWTCLP-4) summarizes the most recent(2015-2017) theoretical and practical knowledge in the field of tropical cyclone(TC) track, intensity, and structure rapid changes at or near landfall. Although the focus of IWTCLPIV was on landfall, this summary necessarily embraces the characteristics of storms during their course over the ocean prior to and leading up to landfall. In the past few years, extremely valuable observational datasets have been collected for TC forecasting guidance and research studies using both aircraft reconnaissance and new geostationary or low-earth orbiting satellites at high temporal and spatial resolution. Track deflections for systems near complex topography such as that of Taiwan and La Réunion have been further investigated, and advanced numerical models with high spatial resolution necessary to predict the interaction of the TC circulation with steep island topography have been developed. An analog technique has been designed to meet the need for longer range landfall intensity forecast guidance that will provide more time for emergency preparedness. Probabilistic track and intensity forecasts have also been developed to better communicate on forecast uncertainty. Operational practices of several TC forecast centers are described herein and some challenges regarding forecasts and warnings for TCs making landfall are identified. This review concludes with insights from both researchers and forecasters regarding future directions to improve predictions of TC track, intensity, and structure at landfall.展开更多
The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its ...The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.展开更多
Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detec...Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.展开更多
The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift...The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things(IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.展开更多
Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of...This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of thermo-poro-elastoplasticity theory is first summarized.The emphasis is put on the effect of pore fluid pressure on plastic deformation.A micromechanics-based elastoplastic model is then presented for a class of clayey rocks considered as host rock.Based on linear and nonlinear homogenization techniques,the proposed model is able to systematically account for the influences of porosity and mineral composition on macroscopic elastic properties and plastic yield strength.The initial anisotropy and time-dependent deformation are also taken into account.The induced cracking process is described by using a non-local damage model.A specific hybrid formulation is proposed,able to conveniently capture tensile,shear and mixed cracks.In particular,the influences of pore pressure and confining stress on the shear cracking mechanism are taken into account.The proposed model is applied to investigating thermo-hydromechanical responses and induced damage evolution in laboratory tests at the sample scale.In the last part,an in situ heating experiment is analyzed by using the proposed model.Numerical results are compared with experimental data and field measurements in terms of temperature variation,pore fluid pressure change and induced damaged zone.展开更多
This article reviews recent advancements,innovative strategies,and the key challenges in Drug Delivery Systems(DDS)for bone regeneration,focusing on tissue engineering.It highlights the limitations of current surgical...This article reviews recent advancements,innovative strategies,and the key challenges in Drug Delivery Systems(DDS)for bone regeneration,focusing on tissue engineering.It highlights the limitations of current surgical interventions forbone regeneration,particularly autogenic bone grafts,and discusses the exploration of alternative materials and methods,including allogeneic and xenogeneic bone grafts,synthetic materials,and biodegradable polymers.The objective is to provide a comprehensive understanding of how contemporary DDS can be optimized and integrated with tissue engineering approaches for more effective bone regeneration therapies.The review explained the mechanisms through which DDS enhance bone repair processes,identifies critical factors influencing their efficacy and safety,and offers an overview of current trends and future perspectives in the field.It emphasizes the need for advanced strategies in bone regeneration that focus on precise control of DDS to address bone conditions such as osteoporosis,trauma,and genetic predispositions leading to fractures.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.展开更多
Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscul...Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.展开更多
The integration of the dynamic adaptive routing(DAR)algorithm in unmanned aerial vehicle(UAV)networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6...The integration of the dynamic adaptive routing(DAR)algorithm in unmanned aerial vehicle(UAV)networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G.DAR’s innovative framework incorporates real-time path adjustments,energy-aware routing,and predictive models,optimizing reliability,latency,and energy efficiency in UAV operations.This study demonstrated DAR’s superior performance in dynamic,large-scale environments,proving its adaptability and scalability for real-time applications.As 6G networks evolve,challenges such as bandwidth demands,global spectrum management,security vulnerabilities,and financial feasibility become prominent.DAR aligns with these demands by offering robust solutions that enhance data transmission while ensuring network reliability.However,obstacles like global route optimization and signal interference in urban areas necessitate further refinement.Future directions should explore hybrid approaches,the integration of machine learning,and comprehensive real-world testing to maximize DAR’s capabilities.The findings underscore DAR’s pivotal role in enabling efficient and sustainable UAV communication systems,contributing to the broader landscape of wireless technology and laying a foundation for the seamless transition to 6G networks.展开更多
基金supported by a grant from the State Scholarship Fund under the China Scholarship Council (No.2003850064)the Chongqing Education Commission (KJ080319)
文摘Objective To explore the immune stimulation effect of recombinant E.coli LLO/OVA on mice bone marrow-derived dendritic cells （BMDCs） and T lymphocytes in vitro.Methods After BMDCs stimulated by E.coli LLO/OVA,their Toll-like receptor （TLR） and nucleotide-binding oligomerization domain （NOD） receptor signalling pathway were examined by superarray hybridization;and the priming effect of the vaccine activated BMDCs on CD4＋T and CD8＋T was determined by [3H]thymidine uptake and ELISA,the tumor cytotoxic effect of activated CD8＋T cells was determined by cytotoxic assay.Results After BMDCs were activated by E.coli LLO/OVA via TLR4,NOD1 receptor and NF-κB signalling pathway,the expression of their surface molecules including MHC class Ⅰ,MHC class Ⅱ,CD40,CD80 and CD86 significantly up-regulated;the secretion of IL-12 and IFN-？ increased also.The mature BMDCs stimulated the allergic CD4＋T and CD8＋T cells proliferation and their IL-2 and IFN-γ secretion,and the activated CD8＋T cells effectively killed B16-OVA melanoma cells and RMA-S/OVA lymphoma cells in vitro.Conclusion E.coli LLO/OVA is effective in inducing BMDCs maturation via activating TLR4 and NOD1 receptor signalling pathway and promoting specific anti-tumor T cell immunity in vitro.
文摘This paper represents a comparative performance evaluation of different diversity combining techniques for a SIMO-OFDM （single-input-multiple-output orthogonal frequency division multiplexing） system over Rayleigh fading channel. OFDM is a key technique for achieving high data rates and spectral efficiency requirements for wireless communication systems. But in scattering environment, the system performances are severely degraded by the effects of multipath fading and inter-symbol interference. In wireless communication systems, antenna diversity is an important technique to combat multipath fading in order to improve the system performance and increase the channel capacity. In this paper, the performance of different diversity combining techniques-SC （selection combining）, EGC （equal gain combining） and MRC （maximal ratio combining） has been analyzed and compared in terms of SNR （signal to noise ratio） and BER （bit error rate） probability. The simulation results show that the maximal ratio combining technique provides maximum performance improvement relative to all other combining schemes by maximizing the SNR of SIMO-OFDM system at the combiner output. The analytic expressions of error probability and effective bit energy to noise ratio correlated with BPSK （binary phase shift keying） modulation have been derived and formulated for N-branch SC, EGC and MRC schemes. The BER characteristics for all three combining techniques are simulated in MATLAB （matrix laboratory） tool box for varying bit energy to noise ratio. Our results also derives that SNR can be improved if the number of receiving antenna is increased, which in turn reduces BER over a Rayleigh fading channel.
文摘The goal of this work is to evaluate and to give evidence to innovative and sustainable technologies applied in the construction industry to carry out self-sufficient energy and to use the surplus energy for the production of hydrogen vector. An architectural integration design along with high technological systems is performed. The intermittency of renewable energy sources along with climatic conditions dependency imposes to store the energy produced, since it is clean and having a big calorific value: the hydrogen vector is currently the better energy carrier. The energy to obtain hydrogen by dissociation of water is supplied by a photovoltaic (PV) system. Through the computations of the annual energy balance between building’s demand and supply energy, it is shown that the extra energy produced by the solar generation system is used also for the hydrogen sustainable mobility. The renewable systems, model’s design and case study are tackled for the bigger one of the Dodecanese islands in the South Aegean Sea: Rhodes (Rodos). The Zero energy building’s integrative design-based approach, applied to the Hotel Buildings type industry is targeted to have new hotels buildings, in the Mediterranean typical warm climate, with zero energy consumption. The designers, authors of this work, have studied a real case or pilot project of an hotel, in the resort formula, suitable to the Greek landscape, showcasing technologies and innovations supporting environmental sustainability, energy efficiency, use of renewable energy, electricity storage by fuel cells that are tools particularly applicable to hotel facility [1]. The feasibility of this case study or pilot project is aligned jointly to the target of Zero Emission and Energy Efficiency EU Policy, as imposed by EU Directives. The strategic position of Rhodes in a geographical point full of sun and wind renewable energy power, enables to ensure the clean energy production, the current interesting development of the hydrogen as energy vector in the buildings [2] and also to satisfy the demand of tourists’ accommodation by having at the same time zero energy costs. Moreover, the presence in the island of the best example worldwide of ancient and sustainable built environment (UNESCO World Heritage site), represents also the best motivation to give witness there of a zero impact environmental urban development through the adoption of these achieved scientific results for a major success of Zero Energy Buildings.
基金funded by the European Union-NextGenerationEU under the Italian Ministry of University and Research(MUR)National Innovation Ecosystem grant(No.ECS00000041-VITALITY)Universit a degli Studi di Perugia and MUR for support within the project Vitality.The University of Perugia is acknowledged for financial support to the university project“Fondo Ricerca di Ateneo,edizione 2022”.MUR is also thanked for PRIN-PNRR 2022 project(No.P2022XKWH7-CircularWaste)been supported by RUDN University Strategic Academic Leadership Program(R.Luque).
文摘Polyurethanes(PUs)are among the most studied,manufactured,and employed polymers due to their versatility and wide range of applications.However,their synthesis generally relies on toxic,non-renewable,and harmful petroleum-based chemicals.In recent decades,driven by urgent environmental needs,research activities for the development of alternative synthetic routes for their production have significantly increased,especially to find more sustainable raw materials and procedures that,for example,no longer require dangerous solvents.Given these premises,the main purpose of this review is to highlight the most recent advances in the production of bioderived polyurethanes.After briefly discussing the chemistry of polyurethanes,we focused on the generation of bio-polyols and bio-isocyanates from plant oils and lignocellulosic biomass(e.g.lignin and sugars),as well as on the most recent trends in non-isocyanates polyurethanes(NIPUs)production.Discussions on their fields of application will be key to giving readers an overview of the actual capabilities of these materials.This review aims to cover and discuss the most recent contributions appearing in the literature up to the beginning of 2023.
基金This work was supported in part by the NSF[grant number ECS-0501539].
文摘A general class of non-linear large-scale interconnected systems is considered,wherein each subsystem is comprised of a nominal part in a general strict-feedback-like structure and a set of appended dynamics.Parametric and functional uncertainties and time delays are allowed throughout the overall system structure including the nominal strictfeedback-like parts and appended dynamics of each subsystem as well as the non-linear subsystem interconnections.The controller design is based on the dual dynamic highgain scaling technique and provides a robust adaptive delay-independent globally stabilising decentralised output-feedback controller.The disturbance attenuation properties of the proposed output-feedback decentralised controller to an exogenous disturbance input are also analysed and specific conditions under which properties such as Input-toOutput-practical-Stability and asymptotic stabilisation are attained are also discussed.
文摘This review prepared for the fourth International Workshop on Tropical Cyclone Landfall Processes(IWTCLP-4) summarizes the most recent(2015-2017) theoretical and practical knowledge in the field of tropical cyclone(TC) track, intensity, and structure rapid changes at or near landfall. Although the focus of IWTCLPIV was on landfall, this summary necessarily embraces the characteristics of storms during their course over the ocean prior to and leading up to landfall. In the past few years, extremely valuable observational datasets have been collected for TC forecasting guidance and research studies using both aircraft reconnaissance and new geostationary or low-earth orbiting satellites at high temporal and spatial resolution. Track deflections for systems near complex topography such as that of Taiwan and La Réunion have been further investigated, and advanced numerical models with high spatial resolution necessary to predict the interaction of the TC circulation with steep island topography have been developed. An analog technique has been designed to meet the need for longer range landfall intensity forecast guidance that will provide more time for emergency preparedness. Probabilistic track and intensity forecasts have also been developed to better communicate on forecast uncertainty. Operational practices of several TC forecast centers are described herein and some challenges regarding forecasts and warnings for TCs making landfall are identified. This review concludes with insights from both researchers and forecasters regarding future directions to improve predictions of TC track, intensity, and structure at landfall.
文摘The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India,extending up to 1500 km.However,when a receiver encounters a Continuous Wave Interference,its positioning accuracy degrades,or sometimes it even fails to work.Wavelet Packet Transform(WPT)is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers.But the conventional method suffers from threshold drifting and employs inflexible thresholding functions.So,to address these issues,an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding(IPSO-PWPT)is proposed.Firstly,a new parameter adaptive thresholding function is constructed.Then,a new form of inertia weight is presented to enhance the performance of PSO.Later,IPSO is used to optimize the key parameters of WPT.Finally,the implementation of the IPSO-PWPT anti-jamming algorithm is discussed.The performance of the proposed technique is evaluated for various performance metrics in four jamming environments.The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability.Also,the results show the ability of the new thresholding function to process various signals effectively.Furthermore,the findings reveal that the improved PSO outperforms the variants of PSO.
基金supported by the Macao Science and Technology Development Fund(FDCT)(Nos.FDCT 0029/2021/A1,FDCT0002/2021/AKP,004/2023/SKL,0036/2021/APD)University of Macao(No.MYRG-GRG2023-00034-IME,SRG2024-00057IME)+2 种基金Dr.Stanley Ho Medical Development Foundation(No.SHMDF-OIRFS/2024/001)Zhuhai Huafa Group(No.HF-006-2021)Guangdong Science and Technology Department(No.2022A0505030022)。
文摘Rapid diagnosis of Salmonella is crucial for the effective control of food safety incidents, especially in regions with poor hygiene conditions. Polymerase chain reaction(PCR), as a promising tool for Salmonella detection, is facing a lack of simple and fast sensing methods that are compatible with field applications in resource-limited areas. In this work, we developed a sensing approach to identify PCR-amplified Salmonella genomic DNA with the naked eye in a snapshot. Based on the ratiometric fiuorescence signals from SYBR Green Ⅰ and Hydroxyl naphthol blue, positive samples stood out from negative ones with a distinct color pattern under UV exposure. The proposed sensing scheme enabled highly specific identification of Salmonella with a detection limit at the single-copy level. Also, as a supplement to the intuitive naked-eye visualization results, numerical analysis of the colored images was available with a smartphone app to extract RGB values from colored images. This work provides a simple, rapid, and user-friendly solution for PCR identification, which promises great potential in molecular diagnosis of Salmonella and other pathogens in field.
文摘The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things(IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金supported by the French National Agency for radioactive waste management(ANDRA).
文摘This work is devoted to numerical analysis of thermo-hydromechanical problem and cracking process in saturated porous media in the context of deep geological disposal of radioactive waste.The fundamental background of thermo-poro-elastoplasticity theory is first summarized.The emphasis is put on the effect of pore fluid pressure on plastic deformation.A micromechanics-based elastoplastic model is then presented for a class of clayey rocks considered as host rock.Based on linear and nonlinear homogenization techniques,the proposed model is able to systematically account for the influences of porosity and mineral composition on macroscopic elastic properties and plastic yield strength.The initial anisotropy and time-dependent deformation are also taken into account.The induced cracking process is described by using a non-local damage model.A specific hybrid formulation is proposed,able to conveniently capture tensile,shear and mixed cracks.In particular,the influences of pore pressure and confining stress on the shear cracking mechanism are taken into account.The proposed model is applied to investigating thermo-hydromechanical responses and induced damage evolution in laboratory tests at the sample scale.In the last part,an in situ heating experiment is analyzed by using the proposed model.Numerical results are compared with experimental data and field measurements in terms of temperature variation,pore fluid pressure change and induced damaged zone.
文摘This article reviews recent advancements,innovative strategies,and the key challenges in Drug Delivery Systems(DDS)for bone regeneration,focusing on tissue engineering.It highlights the limitations of current surgical interventions forbone regeneration,particularly autogenic bone grafts,and discusses the exploration of alternative materials and methods,including allogeneic and xenogeneic bone grafts,synthetic materials,and biodegradable polymers.The objective is to provide a comprehensive understanding of how contemporary DDS can be optimized and integrated with tissue engineering approaches for more effective bone regeneration therapies.The review explained the mechanisms through which DDS enhance bone repair processes,identifies critical factors influencing their efficacy and safety,and offers an overview of current trends and future perspectives in the field.It emphasizes the need for advanced strategies in bone regeneration that focus on precise control of DDS to address bone conditions such as osteoporosis,trauma,and genetic predispositions leading to fractures.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
基金funded by the deanship of scientific research(DSR),King Abdulaziz University,Jeddah,under grant No.(G-1436-611-309).
文摘Cardiovascular diseases(CVDs)remain one of the foremost causes of death globally;hence,the need for several must-have,advanced automated diagnostic solutions towards early detection and intervention.Traditional auscultation of cardiovascular sounds is heavily reliant on clinical expertise and subject to high variability.To counter this limitation,this study proposes an AI-driven classification system for cardiovascular sounds whereby deep learning techniques are engaged to automate the detection of an abnormal heartbeat.We employ FastAI vision-learner-based convolutional neural networks(CNNs)that include ResNet,DenseNet,VGG,ConvNeXt,SqueezeNet,and AlexNet to classify heart sound recordings.Instead of raw waveform analysis,the proposed approach transforms preprocessed cardiovascular audio signals into spectrograms,which are suited for capturing temporal and frequency-wise patterns.The models are trained on the PASCAL Cardiovascular Challenge dataset while taking into consideration the recording variations,noise levels,and acoustic distortions.To demonstrate generalization,external validation using Google’s Audio set Heartbeat Sound dataset was performed using a dataset rich in cardiovascular sounds.Comparative analysis revealed that DenseNet-201,ConvNext Large,and ResNet-152 could deliver superior performance to the other architectures,achieving an accuracy of 81.50%,a precision of 85.50%,and an F1-score of 84.50%.In the process,we performed statistical significance testing,such as the Wilcoxon signed-rank test,to validate performance improvements over traditional classification methods.Beyond the technical contributions,the research underscores clinical integration,outlining a pathway in which the proposed system can augment conventional electronic stethoscopes and telemedicine platforms in the AI-assisted diagnostic workflows.We also discuss in detail issues of computational efficiency,model interpretability,and ethical considerations,particularly concerning algorithmic bias stemming from imbalanced datasets and the need for real-time processing in clinical settings.The study describes a scalable,automated system combining deep learning,feature extraction using spectrograms,and external validation that can assist healthcare providers in the early and accurate detection of cardiovascular disease.AI-driven solutions can be viable in improving access,reducing delays in diagnosis,and ultimately even the continued global burden of heart disease.
文摘The integration of the dynamic adaptive routing(DAR)algorithm in unmanned aerial vehicle(UAV)networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G.DAR’s innovative framework incorporates real-time path adjustments,energy-aware routing,and predictive models,optimizing reliability,latency,and energy efficiency in UAV operations.This study demonstrated DAR’s superior performance in dynamic,large-scale environments,proving its adaptability and scalability for real-time applications.As 6G networks evolve,challenges such as bandwidth demands,global spectrum management,security vulnerabilities,and financial feasibility become prominent.DAR aligns with these demands by offering robust solutions that enhance data transmission while ensuring network reliability.However,obstacles like global route optimization and signal interference in urban areas necessitate further refinement.Future directions should explore hybrid approaches,the integration of machine learning,and comprehensive real-world testing to maximize DAR’s capabilities.The findings underscore DAR’s pivotal role in enabling efficient and sustainable UAV communication systems,contributing to the broader landscape of wireless technology and laying a foundation for the seamless transition to 6G networks.
